Operational Reports

Support Portal provides five operational report types — Conversations, Agents, Labels, Inbox, and Teams — that share a common set of performance metrics but organize data along different dimensions. Together, they provide a comprehensive view of how conversations flow through the system, how efficiently they are handled, and where operational bottlenecks exist.

Operational objective

Operational reports answer historical performance questions that inform staffing decisions, process improvements, and accountability reviews:

• What is the volume of inbound conversations over a given period, and how does it trend?
• How quickly are agents responding to initial contact, and how long does resolution take?
• Which inboxes, teams, or conversation categories experience the longest wait times?
• Are individual agents performing within expected thresholds?

For real-time monitoring of current queue state and agent availability, see Overview Report.

Accessing operational reports

Navigate to Reports in the primary navigation sidebar. The available report types are listed in the submenu:

• Conversations — Aggregate metrics across all conversations.
• Agents — Metrics segmented by individual agent.
• Labels — Metrics segmented by conversation label.
• Inbox — Metrics segmented by inbox.
• Teams — Metrics segmented by team.

Select any report type to load the reporting interface. All five reports share the same layout: filter controls at the top, metric selection tabs, and a time-series graph for the active metric.

Report types

Conversations report

The Conversations report presents aggregate performance data across all conversations within the selected time range. It serves as the primary health indicator for overall service delivery — total conversation volume, average response times, and resolution throughput.

Use this report to identify macro trends: seasonal volume increases, sustained changes in resolution time, or shifts in message throughput that may indicate changes in contact behavior or agent capacity.

Agents report

The Agents report segments the same metrics by individual agent. This enables direct comparison of agent-level performance across response time, resolution time, and message volume.

Operations supervisors can use this report to identify agents who consistently exceed performance thresholds as well as those who may benefit from additional training or workload rebalancing. When read alongside the Conversations report, agent-level data reveals whether systemic trends are driven by specific individuals or reflect organization-wide patterns.

Labels report

The Labels report groups metrics by the Labels applied to conversations. When labeling conventions are consistently maintained, this report becomes a powerful categorization tool — revealing how different types of inquiries (billing, technical support, account management, compliance requests) perform relative to each other.

In a telecom environment, this report can surface whether network-related inquiries take longer to resolve than billing questions. In a government context, it can distinguish between routine information requests and complex case-based interactions that require extended handling.

Inbox report

The Inbox report segments metrics by inbox, providing visibility into channel-level performance. Because each inbox corresponds to a communication channel or service entry point, this report reveals how service quality varies across channels.

Use this report to determine whether specific channels (e.g., WhatsApp, email, live chat) experience longer response times than others, or whether conversation volume distribution across channels aligns with staffing allocation.

Teams report

The Teams report segments metrics by team assignment. Organizations that structure agents into functional teams — first-line support, escalation, specialized product teams — can use this report to measure how each team contributes to overall service delivery.

Comparing team-level resolution times against volume helps identify teams that may be under-resourced relative to their workload, or teams where process improvements could yield the greatest efficiency gains.

Shared metrics

All five report types present the same set of metrics. Select a metric tab to display the corresponding time-series graph.

Conversations

The total number of conversations created during the selected period. Only conversations originating within the date range are counted — reopened conversations from a prior period are excluded.

This metric establishes the baseline volume against which all other metrics are interpreted. A sudden increase may indicate a service event, marketing campaign, or external trigger generating inbound contacts.

First Response Time

The average elapsed time between conversation creation (or handoff from an automated agent to a human agent) and the first human agent response. This metric is calculated across all conversations created within the selected period.

First Response Time is one of the most operationally significant metrics. In environments with defined service level targets, sustained increases in FRT signal a capacity constraint or routing inefficiency that requires intervention. See Service Level Agreements for formal SLA tracking.

Customer waiting time

The average time contacts wait for any agent response — not limited to the first response. This metric is computed across all outgoing messages within the period.

While First Response Time measures initial engagement, Customer Waiting Time reflects the ongoing responsiveness of the service operation. Elevated waiting times may indicate that agents are handling too many concurrent conversations, or that complex interactions are causing delays across the queue.

Resolution Time

The average elapsed time from conversation creation to resolution. If a conversation is reopened and subsequently re-resolved, the resolution time reflects the total elapsed duration from original creation to final resolution — reopens are not counted as separate conversations.

Resolution Time is a lagging indicator that reflects the full lifecycle of a conversation. Extended resolution times may be acceptable for complex cases but should be investigated when they appear consistently across routine inquiries.

Resolution Count

The number of conversations resolved within each day (or grouping interval) of the selected period. Unlike the Conversations metric, which counts creation, Resolution Count tracks closure.

Comparing Resolution Count against conversation creation volume reveals whether the operation is keeping pace with inbound demand or accumulating a backlog.

Messages received

The total number of inbound messages received across all channels during the selected period.

Messages sent

The total number of outbound messages sent from the account during the selected period. This count includes messages dispatched by both human agents and automated systems.

Filtering and grouping

Duration filter

Select a date range to define the reporting window. The default range covers the preceding seven days. Available ranges span from single-day views to multi-month periods.

Data grouping

Data points within the time-series graph can be grouped by time interval. The available grouping options depend on the selected date range:

Adjusting the grouping interval smooths short-term fluctuations to reveal longer-term trends, or preserves daily granularity for detailed operational analysis.

Trend indicator

Each metric displays a trend percentage comparing the current period against the immediately preceding period of equal length. The calculation follows:

Trend = ((Current period value - Previous period value) / Previous period value) × 100

A positive trend indicates an increase; a negative trend indicates a decrease. Interpretation depends on the metric: an upward trend in Resolution Count is typically favorable, while an upward trend in Resolution Time warrants investigation.

Business hours adjustment

A toggle in the upper-right area of the report interface adjusts metric calculations to account for configured business hours. When enabled, time-based metrics (First Response Time, Customer Waiting Time, Resolution Time) exclude periods outside the defined operating schedule.

This adjustment is essential for organizations that do not operate on a 24/7 basis. Without it, overnight and weekend hours inflate time-based metrics and misrepresent actual agent performance.

Interpreting report data

Reading the graph

Hover over any data point in the time-series graph to display the exact value and, for time-based metrics, the number of conversations used in the calculation. This detail helps distinguish between meaningful trends and statistical artifacts caused by low conversation volume on a particular day.

Cross-report analysis

The greatest analytical value emerges from reading multiple report types together:

• Conversations + Agents — Determine whether high-volume periods correspond with specific agents being overloaded or whether workload is distributed effectively.
• Labels + Resolution Time — Identify conversation categories that consistently take longer to resolve, informing knowledge base improvements or Automation refinements.
• Inbox + First Response Time — Detect channels where initial response lags behind others, potentially due to staffing gaps or routing configuration issues.
• Teams + Resolution Count — Evaluate whether team-level throughput aligns with assigned responsibilities and capacity targets. See Agent Capacity for capacity configuration.

Governance implications

Operational reports generate the historical data foundation for governance and accountability:

• Performance documentation — Agent and team reports provide quantitative evidence for performance reviews, training assessments, and resource allocation decisions.
• Service level validation — Time-based metrics serve as the underlying data for SLA compliance analysis. See SLA Reports for dedicated SLA monitoring.
• Trend accountability — Period-over-period trends create an auditable record of service quality trajectory, supporting executive reporting and regulatory compliance documentation.
• Operational capacity planning — Conversation volume trends and resolution throughput inform staffing models and shift planning.
• Export and archival — Report data can be exported for integration with external business intelligence tools, long-term archival, or inclusion in compliance submissions.